# evaluating-code-models

Runs code-model benchmarks and reports pass@k metrics. Use it to compare models, test language coverage, or measure generated code quality.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 953 installs · ★ 13984
- Author: Orchestra Research
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/11-evaluation/bigcode-evaluation-harness/SKILL.md
- Updated: 2026-10-11
- HTML: https://affpapa.org/en/nevibecode/skills/evaluating-code-models

## What it does

This skill connects the BigCode Evaluation Harness to benchmark code generation models on HumanEval, MBPP, HumanEval+, MBPP+, MultiPL-E, and other suites. It guides task selection, generation settings, code execution, and pass@k analysis. MultiPL-E covers 18 languages, including Python, JavaScript, Java, C++, Go, and Rust.

The workflow uses `accelerate launch main.py` with model, temperature, sample-count, and batch-size options. Results can be written to JSON with `--metric_output_path`. The listed dependencies are `bigcode-evaluation-harness`, `transformers>=4.25.1`, `accelerate>=0.13.2`, and `datasets>=2.6.1`. Because generated code can be executed, check `--allow_code_execution` before running an evaluation; the described MultiPL-E flow uses Docker.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill evaluating-code-models -a claude-code
```
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill bigcode-evaluation-harness -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/bigcode-evaluation-harness/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/bigcode-evaluation-harness/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill bigcode-evaluation-harness -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/bigcode-evaluation-harness/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill bigcode-evaluation-harness -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/bigcode-evaluation-harness/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install Orchestra-Research/AI-Research-SKILLs bigcode-evaluation-harness
```

2. **Manually: put the files in place**
- File: `.github/skills/bigcode-evaluation-harness/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill bigcode-evaluation-harness -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/Orchestra-Research/AI-Research-SKILLs --path 11-evaluation/bigcode-evaluation-harness
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/bigcode-evaluation-harness/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill bigcode-evaluation-harness -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/11-evaluation/bigcode-evaluation-harness/SKILL.md

## When to use it
- benchmark models on HumanEval and MBPP
- calculate pass@1, pass@10, and pass@100
- test code generation across 18 languages
- save benchmark results as JSON
- evaluate instruction-tuned models

## Example prompts
- Install the dependencies, then run `bigcode/starcoder2-7b` on `humaneval` and save the output to `results/starcoder2-humaneval.json`.
- Compare `bigcode/starcoder2-7b` with `codellama/CodeLlama-7b-hf` on `humaneval,mbpp` using the same generation settings.
- Run a MultiPL-E evaluation for Python, JavaScript, Java, and C++ by generating without execution, then evaluate the solutions in Docker.

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: d6dcab8f7afcfaf5.

BEGIN THIRD-PARTY FILE d6dcab8f7afcfaf5
````text
---
name: evaluating-code-models
description: Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Evaluation, Code Generation, HumanEval, MBPP, MultiPL-E, Pass@k, BigCode, Benchmarking, Code Models]
dependencies: [bigcode-evaluation-harness, transformers>=4.25.1, accelerate>=0.13.2, datasets>=2.6.1]
---

# BigCode Evaluation Harness - Code Model Benchmarking

## Quick Start

BigCode Evaluation Harness evaluates code generation models across 15+ benchmarks including HumanEval, MBPP, and MultiPL-E (18 languages).

**Installation**:
```bash
git clone https://github.com/bigcode-project/bigcode-evaluation-harness.git
cd bigcode-evaluation-harness
pip install -e .
accelerate config
```

**Evaluate on HumanEval**:
```bash
accelerate launch main.py \
  --model bigcode/starcoder2-7b \
  --tasks humaneval \
  --max_length_generation 512 \
  --temperature 0.2 \
  --n_samples 20 \
  --batch_size 10 \
  --allow_code_execution \
  --save_generations
```

**View available tasks**:
```bash
python -c "from bigcode_eval.tasks import ALL_TASKS; print(ALL_TASKS)"
```

## Common Workflows

### Workflow 1: Standard Code Benchmark Evaluation

Evaluate model on core code benchmarks (HumanEval, MBPP, HumanEval+).

**Checklist**:
```
Code Benchmark Evaluation:
- [ ] Step 1: Choose benchmark suite
- [ ] Step 2: Configure model and generation
- [ ] Step 3: Run evaluation with code execution
- [ ] Step 4: Analyze pass@k results
```

**Step 1: Choose benchmark suite**

**Python code generation** (most common):
- **HumanEval**: 164 handwritten problems, function completion
- **HumanEval+**: Same 164 problems with 80× more tests (stricter)
- **MBPP**: 500 crowd-sourced problems, entry-level difficulty
- **MBPP+**: 399 curated problems with 35× more tests

**Multi-language** (18 languages):
- **MultiPL-E**: HumanEval/MBPP translated to C++, Java, JavaScript, Go, Rust, etc.

**Advanced**:
- **APPS**: 10,000 problems (introductory/interview/competition)
- **DS-1000**: 1,000 data science problems across 7 libraries

**Step 2: Configure model and generation**

```bash
````
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